Tutorials - October 5, 2017

T1: Successful
Data Stewardship in Financial Services David
Plotkin, Director of Metadata Services,
MUFG
More
and more companies in financial services are coming to terms with the fact that
they need to have a data stewardship function in order to be effective in
collecting metadata (including definitions, derivations, and data quality
rules), MDM, and data quality. A lot has been said and spoken about why
stewardship is important, but HOW do you do it and be effective with limited
resources (in the “real world”)? This presentation details how a to set up a
data stewardship in the financial sector, how to run it, the details of
managing the stewardship committee, recruiting help from IT for technical
stewards, what the duties of the data stewards are, and what decisions they
have to make. In addition, it covers how to staff projects for
stewardship and funnel the information from projects back into the overall
stewardship process.

You
will learn:

The
relationship between governance and stewardship

How
to build a governance and stewardship organization in financial services

Data Lakes are
becoming increasingly common in financial services companies, and they present
a new level of challenges for Data Governance. Data Governance is essential
for the success of a Data Lake, but must overcome a number of challenges. For
instance, financial services companies have to a wide diversity of data that
must be cataloged within the Data Lake, and must protect private and
confidential data. This tutorial describes the tasks that a Data Governance
function must perform for a Data Lake in finance, and the capabilities that Data Governance must
develop to the requisite level of maturity. Particular attention is paid
to Data Acquisition of financial data, Data Preparation (“Wrangling”) and the
needs of analytical models. The relationships that Data Governance must
establish with a wide range of units are described including relationships with
Legal, Procurement, Risk, Compliance, Privacy, IT Security, Data Scientists,
Data Architecture, and more. Overall, Data Lakes in financial
institutions are driving Data Governance to play a coordinating and harmonizing
role, which can be considered as a new way of working for Data Governance.

Attendees will learn:

The fundamental
architecture of a Data Lake in a financial institution, and the special data
management needs it has that Data Governance must address

The tasks that Data
Governance must perform to ensure a Data Lake is successful

How to develop the
capabilities that Data Governance needs for its enhanced role in financial Data Lakes

How to deal with the
many different functions that are involved in the Data Lake, and how
Data Governance plays a leading role in ensuring the success of the Data Lake for financial
services

Semantic Data Governance for Regulatory ComplianceRalph Hodgson,
CTO,
TopQuadrant
Across industry sectors, understanding, assessing and reporting for regulatory compliance is both a priority and a challenge for many organizations. Data-related laws, such as HIPAA, BASEL and GDPR, require an understanding of the sources, flows and destinations of data.

Based on W3C semantic standards, TopBraid Enterprise Data Governance (TopBraid EDG) provides a solution that uses and confirms the power of semantics to map data landscapes. Based on cases of use from the financial sector we will show how to consolidate, organize and manage metadata and relationships for compliance assessment.

Using GDPR compliance for illustration, we will demonstrate how TopBraid EDG addresses the problems in regulatory compliance by using models (standards-based ontologies) to:

manage the articles and structure of compliance requirements

provide a reasoning foundation for interpreting the meanings of regulations on data-in-context

describe data and how it flows through software executables in the business environment.

Key capabilities of the solution include: query and visualization to navigate data dependencies; and rules on the data to infer what regulatory obligations are applicable. We will report on a data lineage methodology and best practices inspired by customer projects.

Creating Business Value through Data GovernanceOren Borenstein,
Director,
TD AmeritradeDave Nolting,
Senior Data Governance & Enterprise Information Consultant,
Knowledgent
Mitigating firm-wide risk and driving business value through the creation of a data governance capability is not a small feat, but has become even more important as data creates the path forward for new business strategies. We will discuss how TDA evolved their data governance program and accelerated internal auditing for regulatory implications by learning from the past and leveraging best practices.

Data Governance and Data Quality: Two Sides of the Same CoinKeith Kohl,
VP Product Management,
Trillium Software Data Governance and Data Quality are intrinsically linked, and as the strategic importance of data grows in an organization, the intersection of these practices grows in importance too. This session will outline the connections and dependencies between Data Governance and Data Quality, and provide a practical approach for combining the two to deliver to the business data that is accurate, trusted and fit for purpose. Examples of how to use data governance tools with leading data quality solutions like Trillium Software will be presented.

Learning Objectives:

Discover the connections and dependencies between Data Governance and Data Quality

T4: Governance Artifacts for Financial OrganizationsRobert
S. Seiner, President/Publisher, KIK
Consulting/TDAN.com
When
building a Data Governance program for a financial organization, there are two
different ways to collect and use the metadata that moves the organization
toward formalized accountability. Organizations can purchase or use software
tools that are currently available on the market or they can build tools
internally specific to their programs requirements. It is less expensive to
start by building your own tools.

In
this session with Bob Seiner, he will share several governance artifacts (tools
that you can develop yourself) that are specifically focused on the needs of a
financial organization. These artifacts can be used to enact stewards, engage
them in governed processes, and communicate and enforce compliance and privacy
rules. Attend this session and you will walk away with tangible tools to move
your data governance program forward.

T5: Successfully Meeting the Challenge of the New Global Data Protection Regulation
(GDPR)Malcolm
Chisholm, Chief
Innovation Officer, First
San Francisco Partners
A
sweeping, new privacy regulation arrives May 25, 2018. Will you be ready for
it? The General Data Protection Regulation (GDPR) impacts all companies that
do business in the EU, including many financial services companies domiciled in
the US. GDPR replaces the previous Data Protection Directive (95/46/EC), and
extends the protections for individuals’ personal data. Significant fines may
be levied for noncompliance. This tutorial describes the steps an
organization affected by GDPR must take in the time remaining to assure
compliance. These steps require the coordination of multiple enterprise
capabilities such as Information Security, Internal Audit, Privacy, and Legal. The role of Data Governance in coordinating the required activities is
described.

Attendees
to the tutorial will be provided with insight and approaches for:

Understanding
where GDPR fits into the global data privacy and data protection landscape,
what the scope of GDPR is, and why GDPR is so important

Adopting
a GDPR readiness assessment process to help identify potential gaps and a
roadmap to address those gaps

How
Data Governance works to coordinate the response to GDPR, and how this may be
extensive to global data privacy and data protection regulations in general

Developing
a Data Governance program framework that embeds data privacy and data
protection processes and tools

T6: How to Develop Data Quality and Data Governance MetricsBarbara Deemer, VP Financial Systems, NavientMichele Koch, Director, Enterprise Data Intelligence, Navient
Warren Buffett said "Beware of geeks
bearing formulas". Good advice to live by but most of us implementing Data
Governance and Data Quality Programs need to be geeks some of the time in order
to develop formulas to derive metrics to market and sustain our programs. This
tutorial will provide a detailed, step-by step account of Navient’s successful
approach to developing program metrics associated with their award winning
enterprise Data Governance and Data Quality Programs. It will also cover
deriving business value metrics by quantifying the impacts to generating
revenue and avoiding costs.

Topics that will be covered include:

Overview of DG Program concepts to set
the stage for tracking and reporting DG metrics

Engaging the Data Governance Council and
identifying business approvers for metrics

For many financial services organizations, Data Governance programs have been tactically focused on complying with changing regulations such as BCBS 239 and GDPR. This keynote will make the case for thinking more strategically about Data Governance and its integration with other key areas in data architecture; and how an organization can effectively approach implementation and meet regulatory goals and objectives.